Healthy #fondue #dinner

Abstract

Social media generate large-scale data to study food and drink consumption in everyday life. Using Instagram posts in Switzerland over five years, our goal is two-fold. First, we extract key food & drink consumption patterns, through the lenses of a data-driven dictionary of popular items extracted from hashtags, and of a food categorization system used by the Swiss Federal government for national statistics purposes. Patterns related to spatial and temporal distributions of food & drink consumption, demographics, and eating events are extracted and compared to official statistics. Second, using the insights from this analysis, we define two eating event classification tasks, including a two-class task (healthy vs. unhealthy) and a six-class task (the three main meals break-fast/lunch/dinner/plus brunch/coffee/tea). Both tasks use hash-tags as labels for supervised learning. We study how content (hashtags and food categories), context (time and location), and social features (likes) can discriminate these eating events. A random forest and a combination of content and context features can classify healthy vs. unhealthy eating posts with 85.8% accuracy, and the six daily eating occasions with 61.7% accuracy.